Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

A Fast and Reliable State Estimation Algorithm for AEP's New Control Center

Abstract: This paper presents an evaluation of some previously proposed and newly developed state estimation algorithms. One new algorithm, which employs a constant, decoupled gain matrix and certain other simplifying approximations, is shown to be clearly superior to the other methods considered; accordingly this algorithm has been chosen for use in AEP's new control center. The selected algorithm is fast, reliable and can process a measurement set consisting of line flows, bus injections and voltage magnitudes. Test results are given for several real networks and some standard IEEE test systems (having as many as 460 buses and R/X ratios ranging from 0.1 to 2). The network conditions used in the tests range from light load to severe contingencies with low voltages and large phase angles. A wide variety of practical and hypothetical measurement sets are considered.

Noise Analysis for Comparator-Based Circuits

Abstract: Noise analysis for comparator-based circuits is presented. The goal is to gain insight into the different sources of noise in these circuits for design purposes. After the general analysis techniques are established, they are applied to different noise sources in the comparator-based switched-capacitor pipeline analog-to-digital converter (ADC). The results show that the noise from the virtual ground threshold detection comparator dominates the overall ADC noise performance. The noise from the charging current can also be significant, depending on the size of the capacitors used, but the contribution was small in the prototype. The other noise sources have contributions comparable to those in op-amp-based designs, and their effects can be managed through appropriate design. In the prototype, folded flicker noise was found to be a significant contributor to the broadband noise because the flicker noise of the comparator extends beyond the Nyquist rate of the converter.

Unbiased Finite Impluse Response Filtering: An Iterative Alternative to Kalman Filtering Ignoring Noise and Initial Conditions

Abstract: If a system and its observation are both represented in state space with linear equations, the system noise and the measurement noise are white, Gaussian, and mutually uncorrelated, and the system and measurement noise statistics are known exactly; then, a Kalman filter (KF) [1] with the same order as the system provides optimal state estimates in a way that is simple and fast and uses little memory. Because such estimators are of interest for designers, numerous linear and nonlinear problems have been solved using the KF, and many articles about KF applications appear every year. However, the KF is an infinite impulse response (IIR) filter [2]. Therefore, the KF performance may be poor if operational conditions are far from ideal [3]. Researchers working in the field of statistical signal processing and control are aware of the numerous issues facing the use of the KF in practice: insufficient robustness against mismodeling [4] and temporary uncertainties [2], the strong effect of the initial values [1], and high vulnerability to errors in the noise statistics [5]-[7].

An Adaptive Kalman Filter for ECG Signal Enhancement

Abstract: The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a Bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the Bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.